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Journal articles on the topic 'Multicasting (Computer networks) Wireless communication systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

T, SAMPRADEEPRAJ, and RAJA S P. "On Improving Reliability in Multicast Routing Protocol for Wireless Sensor Network." Information Technology And Control 49, no. 2 (2020): 260–74. http://dx.doi.org/10.5755/j01.itc.49.2.24111.

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Multicast routing becomes the most challenging problem in Wireless Sensor Networks (WSN). Multicasting is an effective way to facilitate group communication in which the multicast data need to be sent from a source node to multiple receivers. In this paper, a simple and efficient algorithm Minimum Connected Dominating Set (MCDS) is used to form a virtual backbone as forwarding group of the network. The MCDS aims at minimizing the number of nodes, where few nodes should be dominated, which are responsible for forwarding the multicast packets by applying Random Linear Network Coding (RLNC). RLNC has great potential to improve the performance of multicast routing protocol. The objective of this paper is to improve the performance of On-Demand Multicasting Routing Protocol (ODMRP) with respect to reliability using RLNC over MCDS for WSN, so that bandwidth utilization can be increased in the network. The proposed approach is named as RLNMCDS-ODMRP, which deliver multicast data in high reliable. Experimental results and performance analysis show that the proposed protocol outperforms the classical multicast routing protocols that use MCDS or RLNC.

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2

Kim, Sungwook. "A New Multicasting Device-to-Device Communication Control Scheme for Virtualized Cellular Networks." Wireless Communications and Mobile Computing 2019 (February 13, 2019): 1–9. http://dx.doi.org/10.1155/2019/3540674.

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With the explosion in the number of wireless services, the unprecedented growth of mobile date traffic has brought a heavy burden on the traditional cellular networks. To meet the explosive traffic services, the potential of network virtualization and multicasting device-to-device (MD2D) technology have been proposed as a promising solution for next-generation networks. In this paper, we propose a novel MD2D control scheme for virtualized cellular networks, which enables device clustering for local MD2D services to obtain the finest system performance. By taking into consideration dynamic situations and competitive environments, we formulate our control algorithms as a game model with imperfect system information. Inspired by the incentive mechanism and evolutionary decision process, the proposed game approach can guide selfish mobile devices toward honest behaviors, and the MD2D services are provided based on the step-by-step interactive feedback process. Through numerical evaluation and simulation analysis, we not only quantify the outcome of our proposed scheme’s system throughput, bandwidth utilization, and MD2D service efficiency, but also provide the performance comparison with existing schemes. Finally, we provide further challenges and various opportunities in the research area of MD2D-enabled cellular network operations.

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3

Santhi, H., and N. Jaisankar. "Towards an Effective QoS On Demand Multicast Routing Protocol for Multi-Channel Multi Interface WMNs." International Journal of Business Data Communications and Networking 10, no. 4 (2014): 1–20. http://dx.doi.org/10.4018/ijbdcn.2014100101.

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Wireless Mesh Networks (WMNs) is becoming an emerging paradigm, due to its simple and cost effective deployment, and increased growth in popularity for the next generation wireless Internet. Unlike the single channel, it is challenging to provision a robust multicasting by means of multi-channel and multi-interface WMNs. Therefore, in order to tackle the multicast issues, a high-quality path selection and channel assignment are essential. The proposed Quality of Service – On Demand Multichannel Multicast Routing Protocol (QoS- ODMMRP) includes merged path selection and top-down channel tuning mechanism to support multi-channel and multi-interface WMNs. Thus, the proposed work reduces the number of transmissions and increases the communication throughput efficiently.

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4

Hsiu, Pi-Cheng, Chin-Hsien Wu, and Tei-Wei Kuo. "Maximum-residual multicasting and aggregating in wireless ad hoc networks." Wireless Networks 16, no. 3 (2009): 701–11. http://dx.doi.org/10.1007/s11276-009-0163-4.

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5

Geithner, Thomas, and Fikret Sivrikaya. "Transmission Rate Sampling and Selection for Reliable Wireless Multicast." Wireless Communications and Mobile Computing 2020 (September 29, 2020): 1–22. http://dx.doi.org/10.1155/2020/8850198.

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The multicast communication concept offers a scalable and efficient method for many classes of applications; however, its potential remains largely unexploited when it comes to link-layer multicasting in wireless local area networks. The fundamental lacking feature for this is a transmission rate control mechanism that offers higher transmission performance and lower channel utilization, while ensuring the reliability of wireless multicast transmissions. This is much harder to achieve in a scalable manner for multicast when compared with unicast transmissions, which employs explicit acknowledgment mechanisms for rate control. This article introduces EWRiM, a reliable multicast transmission rate control protocol for IEEE 802.11 networks. It adapts the transmission rate sampling concept to multicast through an aggregated receiver feedback scheme and combines it with a sliding window forward error correction (FEC) mechanism for ensuring reliability at the link layer. An inherent novelty of EWRiM is the close interaction of its FEC and transmission rate selection components to address the performance-reliability tradeoff in multicast communications. The performance of EWRiM was tested in three scenarios with intrinsically different traffic patterns; namely, music streaming scenario, large data frame delivery scenario, and an IoT scenario with frequent distribution of small data packets. Evaluation results demonstrate that the proposed approach adapts well to all of these realistic multicast traffic scenarios and provides significant improvements over the legacy multicast- and unicast-based transmissions.

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6

Shorfuzzaman, Mohammad, Rasit Eskicioglu, and Peter Graham. "In-Network Adaptation of Video Streams Using Network Processors." Advances in Multimedia 2009 (2009): 1–20. http://dx.doi.org/10.1155/2009/905890.

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The increasing variety of networks and end systems, especially wireless devices, pose new challenges in communication support for, particularly, multicast-based collaborative applications. In traditional multicasting, the sender transmits video at the same rate and resolution to all receivers independent of their network characteristics, end system equipment, and users' preferences about video quality and significance. Such an approach results in resources being wasted and may also result in some receivers having their quality expectations unsatisfied. This problem can be addressed, near the network edge, by applying dynamic, in-network adaptation (e.g., transcoding) of video streams to meet available connection bandwidth, machine characteristics, and client preferences. In this paper, we extrapolate from earlier work of Shorfuzzaman et al. 2006 in which we implemented and assessed an MPEG-1 transcoding system on the Intel IXP1200 network processor to consider the feasibility of in-network transcoding for other video formats and network processor architectures. The use of “on-the-fly” video adaptation near the edge of the network offers the promise of simpler support for a wide range of end devices with different display, and so forth, characteristics that can be used in different types of environments.

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7

Papavassiliou, Symeon, and Beongku An. "Supporting multicasting in mobile ad-hoc wireless networks: issues, challenges, and current protocols." Wireless Communications and Mobile Computing 2, no. 2 (2002): 115–30. http://dx.doi.org/10.1002/wcm.26.

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8

Lim, Sung-Hwa, Young-Bae Ko, Cheolgi Kim, and Nitin H. Vaidya. "Design and implementation of multicasting for multi-channel multi-interface wireless mesh networks." Wireless Networks 17, no. 4 (2011): 955–72. http://dx.doi.org/10.1007/s11276-011-0327-x.

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9

Sarker, Dilip Kumar, Md Zahurul Islam Sarkar, and Md Shamim Anower. "Secure wireless multicasting through AF-cooperative networks with best-relay selection over generalized fading channels." Wireless Networks 26, no. 3 (2018): 1717–30. http://dx.doi.org/10.1007/s11276-018-1861-6.

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10

Guo, Song, Minyi Guo, and Victor C. M. Leung. "A message complexity oriented design of distributed algorithm for long-lived multicasting in wireless sensor networks." International Journal of Sensor Networks 6, no. 3/4 (2009): 180. http://dx.doi.org/10.1504/ijsnet.2009.029394.

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11

Wang, Kan, Ruijie Wang, Junhuai Li, and Meng Li. "Joint V2V-Assisted Clustering, Caching, and Multicast Beamforming in Vehicular Edge Networks." Wireless Communications and Mobile Computing 2020 (November 19, 2020): 1–12. http://dx.doi.org/10.1155/2020/8837751.

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As an emerging type of Internet of Things (IoT), Internet of Vehicles (IoV) denotes the vehicle network capable of supporting diverse types of intelligent services and has attracted great attention in the 5G era. In this study, we consider the multimedia content caching with multicast beamforming in IoV-based vehicular edge networks. First, we formulate a joint vehicle-to-vehicle- (V2V-) assisted clustering, caching, and multicasting optimization problem, to minimize the weighted sum of flow cost and power cost, subject to the quality-of-service (QoS) constraints for each multicast group. Then, with the two-timescale setup, the intractable and stochastic original problem is decoupled at separate timescales. More precisely, at the large timescale, we leverage the sample average approximation (SAA) technique to solve the joint V2V-assisted clustering and caching problem and then demonstrate the equivalence of optimal solutions between the original problem and its relaxed linear programming (LP) counterpart; and at the small timescale, we leverage the successive convex approximation (SCA) method to solve the nonconvex multicast beamforming problem, whereby a series of convex subproblems can be acquired, with the convergence also assured. Finally, simulations are conducted with different system parameters to show the effectiveness of the proposed algorithm, revealing that the network performance can benefit from not only the power saving from wireless multicast beamforming in vehicular networks but also the content caching among vehicles.

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12

Bauer, Joanna, Dag Haugland, and Di Yuan. "Analysis and computational study of several integer programming formulations for minimum-energy multicasting in wireless ad hoc networks." Networks 52, no. 2 (2008): 57–68. http://dx.doi.org/10.1002/net.20222.

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13

Park, Jounsup. "Multi-Session Multicasting for 360-Degree Video Multicast over OFDMA Systems." International Journal of Digital Multimedia Broadcasting 2021 (June 16, 2021): 1–19. http://dx.doi.org/10.1155/2021/5560312.

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360-degree video content provides a rich and immersive multimedia experience to viewers by allowing viewers to the video from any angle. However, 360-degree videos require much higher bandwidth to be delivered over mobile networks compared to conventional videos. Multicasting of the videos is one of the solutions to efficiently utilize the limited bandwidth since many viewers share the wireless spectrum resource for popular videos, such as sports events or musical concerts. LTE eMBMS assigns the videos to the video sessions, and multiple viewers can subscribe to the same video allocated to the video sessions. Moreover, the tiling of the 360-degree video makes it possible to control the regional quality of the video. The tiles that are likely to be seen by many viewers should have higher quality than other tiles to satisfy more viewers. In this paper, we proposed the Multi-Session Multicast (MSM) system to optimally allocate the wireless resources to tiles with different qualities to maximize the expected user experience. The experimental results show that the proposed MSM system provides higher quality videos to viewers using limited wireless resources.

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14

Zhang, Long, Wenjing Cao, Xinxin Zhang, and Haitao Xu. "MAC2: Enabling multicasting and congestion control with multichannel transmission for intelligent vehicle terminal in Internet of Vehicles." International Journal of Distributed Sensor Networks 14, no. 8 (2018): 155014771879358. http://dx.doi.org/10.1177/1550147718793586.

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Internet of Vehicles has become a promising way to realize the evolution from vehicular ad hoc networks and next-generation intelligent transportation system into future autonomous driving scenarios, clean-energy intelligent vehicles, and Smart Cities. However, multicasting service messages on available service channels and periodic exchanges of beacon messages on control channel cause the problem of efficiently scheduling those messages via multichannel transmission for intelligent vehicle terminal, to support real-world applications in Internet of Vehicles scenario. In this article, we investigate the intelligent vehicle terminal architecture and, particularly, design the wireless communication board by incorporating multicasting and congestion control modules. Especially, we present a multicast data delivery scheme with random-delay lowest-cost constraint to transfer service messages on service channels. Furthermore, a priority-aware congestion control scheme is also proposed by considering differentiated priorities of beacon messages on control channel, to cope with the congestion problem at bottleneck vehicle node. Based on the proposed schemes, we build up the RanLow (Random-delay Lowest-cost) module and the priority-aware congestion control (PARCEL) module by enabling multicasting and congestion control together in wireless communication board of the intelligent vehicle terminal architecture. Finally, the experimental results and comparison show that our devised RanLow module and PARCEL module are feasible and more efficient than existing schemes.

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15

Ghassemlooy, Z., and A. C. Boucouvalas. "Indoor optical wireless communication systems and networks." International Journal of Communication Systems 18, no. 3 (2005): 191–93. http://dx.doi.org/10.1002/dac.698.

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16

Prasad, R. "Wireless Broadband Communication Systems." IEEE Communications Magazine 35, no. 1 (1997): 18. http://dx.doi.org/10.1109/mcom.1997.568189.

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17

Kawade, S., and T. Hodgkinson. "Licence-exempt wireless communication systems." BT Technology Journal 25, no. 2 (2007): 64–75. http://dx.doi.org/10.1007/s10550-007-0029-1.

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18

Jain, Aman, Sanjeev R. Kulkarni, and Sergio Verdu. "Multicasting in Large Wireless Networks: Bounds on the Minimum Energy Per Bit." IEEE Transactions on Information Theory 57, no. 1 (2011): 14–32. http://dx.doi.org/10.1109/tit.2010.2090228.

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19

Sankaran, C., and A. Ephremides. "The use of multiuser detectors for multicasting in wireless ad hoc CDMA networks." IEEE Transactions on Information Theory 48, no. 11 (2002): 2873–87. http://dx.doi.org/10.1109/tit.2002.804045.

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20

Trivedi, Kishor S., Xiaomin Ma, and S. Dharmaraja. "Performability modelling of wireless communication systems." International Journal of Communication Systems 16, no. 6 (2003): 561–77. http://dx.doi.org/10.1002/dac.605.

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21

Shen, Xuemin. "Green wireless communication networks [Editor's note]." IEEE Network 27, no. 2 (2013): 2–3. http://dx.doi.org/10.1109/mnet.2013.6485087.

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22

Al-Mousawi, Ali Jameel. "Wireless communication networks and swarm intelligence." Wireless Networks 27, no. 3 (2021): 1755–82. http://dx.doi.org/10.1007/s11276-021-02545-x.

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23

Daneshgaran, Fred, and Josef Noll. "Special issue: Reconfigurable wireless communication systems." Wireless Communications and Mobile Computing 2, no. 8 (2002): 785–87. http://dx.doi.org/10.1002/wcm.117.

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24

Lu, Dingqing, and Zhengrong Zhou. "Integrated solutions for testing wireless communication systems." IEEE Communications Magazine 49, no. 6 (2011): 96–100. http://dx.doi.org/10.1109/mcom.2011.5783992.

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25

Chen, Hui, Yang Xiao, Xiaoyan Hong, Fei Hu, and Jiang (Linda) Xie. "A survey of anonymity in wireless communication systems." Security and Communication Networks 2, no. 5 (2009): 427–44. http://dx.doi.org/10.1002/sec.78.

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26

Lee, Jeongcheol, Babar Shah, Giovanni Pau, Javier Prieto, and Ki-Il Kim. "Real-Time Communication in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2018 (2018): 1–2. http://dx.doi.org/10.1155/2018/9612631.

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27

Duong, Trung Q., Chinmoy Kundu, Antonino Masaracchia, and Van-Dinh Nguyen. "Editorial: Reliable Communication for Emerging Wireless Networks." Mobile Networks and Applications 25, no. 1 (2020): 271–73. http://dx.doi.org/10.1007/s11036-019-01508-z.

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28

Bagci, Faruk, N. A. Ala', and N. A. Khalifeh. "Cluster communication protocol for wireless sensor networks." International Journal of Sensor Networks 20, no. 2 (2016): 84. http://dx.doi.org/10.1504/ijsnet.2016.074696.

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29

CHEN, Chien-Sheng, Szu-Lin SU, and Yih-Fang HUANG. "Mobile Location Estimation in Wireless Communication Systems." IEICE Transactions on Communications E94-B, no. 3 (2011): 690–93. http://dx.doi.org/10.1587/transcom.e94.b.690.

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30

Alves, Renan C. A., Doriedson A. G. Oliveira, Geovandro C. C. F. Pereira, Bruno C. Albertini, and Cíntia B. Margi. "WS3N: Wireless Secure SDN-Based Communication for Sensor Networks." Security and Communication Networks 2018 (August 1, 2018): 1–14. http://dx.doi.org/10.1155/2018/8734389.

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The Software Defined Networking (SDN) paradigm can provide flexible routing and potentially support the different communication patterns that exist in Wireless Sensor Networks (WSN). However applying this paradigm to resource-constrained networks is not straightforward, especially if security services are a requirement. Existing SDN-based approaches for WSN evolved over time, addressing resource-constrained requirements. However, they do not integrate security services into their design and implementation. This work’s main contribution is a secure-by-design SDN-based framework for Wireless Sensors Networks. Secure node admission and end-to-end key distribution to support secure communication are considered key services, which the framework must provide. We describe its specification, design, implementation, and experiments considering device and protocol constraints. The results indicate that our approach has achieved such goals with acceptable overheads up to medium sized networks.

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31

Arslan, H�seyin, and Gregory E. Bottomley. "Channel estimation in narrowband wireless communication systems." Wireless Communications and Mobile Computing 1, no. 2 (2001): 201–19. http://dx.doi.org/10.1002/wcm.14.

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32

Zou, Shihong, Yanhong Xu, Honggang Wang, Zhouzhou Li, Shanzhi Chen, and Bo Hu. "A Survey on Secure Wireless Body Area Networks." Security and Communication Networks 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/3721234.

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Combining tiny sensors and wireless communication technology, wireless body area network (WBAN) is one of the most promising fields. Wearable and implantable sensors are utilized for collecting the physiological data to achieve continuously monitoring of people’s physical conditions. However, due to the openness of wireless environment and the significance and privacy of people’s physiological data, WBAN is vulnerable to various attacks; thus, strict security mechanisms are required to enable a secure WBAN. In this article, we mainly focus on a survey on the security issues in WBAN, including securing internal communication in WBAN and securing communication between WBAN and external users. For each part, we discuss and identify the security goals to be achieved. Meanwhile, relevant security solutions in existing research on WBAN are presented and their applicability is analyzed.

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33

Sheikh, A. U. H. "Wireless communication technologies: new multimedia systems [Book Review]." IEEE Communications Magazine 39, no. 11 (2001): 36–52. http://dx.doi.org/10.1109/mcom.2001.965354.

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34

Chen, Xi, Qianchuan Zhao, and Xiaohong Guan. "Energy-Efficient Sensing Coverage and Communication for Wireless Sensor Networks." Journal of Systems Science and Complexity 20, no. 2 (2007): 225–34. http://dx.doi.org/10.1007/s11424-007-9019-0.

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35

Kitamoto, Norio, and Tomoaki Ohtsuki. "Parallel combinatory multiple-subcarrier optical wireless communication systems." International Journal of Communication Systems 18, no. 3 (2005): 195–203. http://dx.doi.org/10.1002/dac.699.

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36

Znati, Taieb F., and Sung Jin Kim. "Adaptive channel management schemes for wireless communication systems." International Journal of Communication Systems 13, no. 6 (2000): 435–60. http://dx.doi.org/10.1002/1099-1131(200009)13:6<435::aid-dac463>3.0.co;2-6.

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37

Ashraf, Manzur, Aruna Jayasuriya, and Sylvie Perreau. "Distributed opportunistic communication protocol for wireless multihop networks." International Journal of Mobile Network Design and Innovation 3, no. 2 (2009): 112. http://dx.doi.org/10.1504/ijmndi.2009.030843.

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38

Wang, Xiumin, Jin Wang, and Shukui Zhang. "Network Coded Wireless Cooperative Multicast with Minimum Transmission Cost." International Journal of Distributed Sensor Networks 8, no. 10 (2012): 614206. http://dx.doi.org/10.1155/2012/614206.

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We study multicasting over wireless lossy links. Instead of downloading all the data from the source node, we allow the destination nodes themselves to locally exchange the packets, as local communication within a cluster achieves higher packet reception probability with less transmission cost. However, when shall we stop the transmission from the source node? If the source stops too early, the destination nodes locally cannot reconstruct all the original packets, while if the source stops too late, the benefit of cooperative data exchange cannot be fully exploited. In this paper, we propose a network coded hybrid source and cooperative exchange scheme to determine when to stop the source sending and start the exchange process, so as to minimize the total transmission cost. For the case when the clusters are predefined, we derive the expected total transmission cost with our hybrid scheme. Our theoretical results show that under a special condition, the source node should keep sending the packets until all the destinations get the complete information. For the case when the clusters are not predefined, we propose a cluster division algorithm such that the destination nodes within each cluster can conduct data exchange locally with energy efficiency. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

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39

Underberg, Lisa. "Hybrid wired-wireless communication networks for factory automation." at - Automatisierungstechnik 68, no. 12 (2020): 1077–78. http://dx.doi.org/10.1515/auto-2020-0150.

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AbstractThis thesis investigates a wireless communication network that is suitable to be deployed as a transparent intermediate wireless network within a hybrid wired-wireless network serving industrial applications with strict timing and reliability requirements. Physical layer and medium access scheme are in focus.

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40

Essai Ali, Mohamed Hassan, and Ibrahim B. M. Taha. "Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach." PeerJ Computer Science 7 (August 26, 2021): e682. http://dx.doi.org/10.7717/peerj-cs.682.

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In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

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41

Shadaram, Mehdi, Mubashir Husain Rehmani, and Vyasa Sai. "Introduction to the special section on “Advancement in Communication Networks: From Wireless Mesh Networks, Wireless Sensor Networks, to Cellular Networks”." Computers & Electrical Engineering 73 (January 2019): 376–77. http://dx.doi.org/10.1016/j.compeleceng.2018.11.019.

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42

Dressler, Falko, Archan Misra, and Rajeev Shorey. "Special Issue: Recent Advances in Wireless Communication Systems." Mobile Networks and Applications 16, no. 5 (2011): 586–88. http://dx.doi.org/10.1007/s11036-011-0299-1.

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43

Unterhuber, Paul, Stephan Pfletschinger, Stephan Sand, et al. "A Survey of Channel Measurements and Models for Current and Future Railway Communication Systems." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7308604.

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Modern society demands cheap, more efficient, and safer public transport. These enhancements, especially an increase in efficiency and safety, are accompanied by huge amounts of data traffic that need to be handled by wireless communication systems. Hence, wireless communications inside and outside trains are key technologies to achieve these efficiency and safety goals for railway operators in a cost-efficient manner. This paper briefly describes nowadays used wireless technologies in the railway domain and points out possible directions for future wireless systems. Channel measurements and models for wireless propagation are surveyed and their suitability in railway environments is investigated. Identified gaps are pointed out and solutions to fill those gaps for wireless communication links in railway environments are proposed.

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44

Cho, Kang-Hee, Si-Hyeon Lee, and Vincent Y. F. Tan. "Throughput Scaling of Covert Communication Over Wireless Adhoc Networks." IEEE Transactions on Information Theory 66, no. 12 (2020): 7684–701. http://dx.doi.org/10.1109/tit.2020.3011895.

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45

Thompson, John, Xiaohu Ge, Hsiao-Chun Wu, et al. "5G wireless communication systems: prospects and challenges [Guest Editorial]." IEEE Communications Magazine 52, no. 2 (2014): 62–64. http://dx.doi.org/10.1109/mcom.2014.6736744.

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46

Li, Ji, Jean Conan, and Samuel Pierre. "Position location of mobile terminal in wireless MIMO communication systems." Journal of Communications and Networks 9, no. 3 (2007): 254–64. http://dx.doi.org/10.1109/jcn.2007.6182853.

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47

Kwon, Hojoong, Soomin Ko, Hanbyul Seo, and Byeong Gi Lee. "Inter-cell interference management for next-generation wireless communication systems." Journal of Communications and Networks 10, no. 3 (2008): 258–67. http://dx.doi.org/10.1109/jcn.2008.6388347.

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48

Patra, Jagdish C., Pramod K. Meher, and Goutam Chakraborty. "Nonlinear channel equalization for wireless communication systems using Legendre neural networks." Signal Processing 89, no. 11 (2009): 2251–62. http://dx.doi.org/10.1016/j.sigpro.2009.05.004.

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49

Love, David, Robert Heath, Vincent N. Lau, David Gesbert, Bhaskar Rao, and Matthew Andrews. "An overview of limited feedback in wireless communication systems." IEEE Journal on Selected Areas in Communications 26, no. 8 (2008): 1341–65. http://dx.doi.org/10.1109/jsac.2008.081002.

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50

Ramirez-Iniguez, R., and R. J. Green. "Optical antenna design for indoor optical wireless communication systems." International Journal of Communication Systems 18, no. 3 (2005): 229–45. http://dx.doi.org/10.1002/dac.701.

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